Our Contributions. We propose a new framework called generative optimization networks (GON).The code and datasets are available on GitHub at https://github.com/imperial-qore/GON. This is similar to generative adversarial nets (GAN) [1]}, but without the generator. The novel insight of our work is that a sufficiently trained discriminator should not only indicate whether an input belongs to a data distribution, but also how to tweak the input to make it resemble the target distribution more closely. This can be done using the gradient of the discriminator output with respect to its input [2]} and executing gradient optimization of the output score. Having only a single network should give significant memory footprint gains, at the cost of higher training times (as generation would entail executing optimization in the input space). This is acceptable in many applications scenarios where initial model training is only a one-time activity [3]}, [4]}. The GON framework is agnostic to the specific use case and can be utilized in any application where GANs are used. In this article, we explore the important use case of anomaly detection in memory-constrained edge devices, specifically Raspberry Pis. A broader impact statement is given in Appendix .
